Overview

Dataset statistics

Number of variables16
Number of observations13543
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory128.0 B

Variable types

Numeric16

Alerts

Area is highly correlated with Perimeter and 12 other fieldsHigh correlation
Perimeter is highly correlated with Area and 13 other fieldsHigh correlation
MajorAxisLength is highly correlated with Area and 12 other fieldsHigh correlation
MinorAxisLength is highly correlated with Area and 11 other fieldsHigh correlation
AspectRation is highly correlated with Area and 13 other fieldsHigh correlation
Eccentricity is highly correlated with Area and 13 other fieldsHigh correlation
ConvexArea is highly correlated with Area and 12 other fieldsHigh correlation
EquivDiameter is highly correlated with Area and 12 other fieldsHigh correlation
Solidity is highly correlated with Perimeter and 2 other fieldsHigh correlation
roundness is highly correlated with Area and 14 other fieldsHigh correlation
Compactness is highly correlated with Area and 13 other fieldsHigh correlation
ShapeFactor1 is highly correlated with Area and 11 other fieldsHigh correlation
ShapeFactor2 is highly correlated with Area and 12 other fieldsHigh correlation
ShapeFactor3 is highly correlated with Area and 13 other fieldsHigh correlation
ShapeFactor4 is highly correlated with Area and 11 other fieldsHigh correlation
Extent is highly correlated with AspectRation and 4 other fieldsHigh correlation
MajorAxisLength has unique values Unique
MinorAxisLength has unique values Unique
AspectRation has unique values Unique
Eccentricity has unique values Unique
roundness has unique values Unique
Compactness has unique values Unique
ShapeFactor1 has unique values Unique
ShapeFactor2 has unique values Unique
ShapeFactor3 has unique values Unique
ShapeFactor4 has unique values Unique

Reproduction

Analysis started2022-11-02 21:39:33.498881
Analysis finished2022-11-02 21:39:54.635379
Duration21.14 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Area
Real number (ℝ)

HIGH CORRELATION

Distinct12011
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-2.544113169
Maximum2.543527285
Zeros0
Zeros (%)0.0%
Negative7035
Negative (%)51.9%
Memory size105.9 KiB
2022-11-02T17:39:54.686881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.544113169
5-th percentile-1.6244325
Q1-0.7245389724
median-0.05271276157
Q30.799370421
95-th percentile1.473718232
Maximum2.543527285
Range5.087640454
Interquartile range (IQR)1.523909393

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)nan
Kurtosis-0.5329761666
Mean0
Median Absolute Deviation (MAD)0.7501886897
Skewness0.1138324874
Sum9.094947018 × 10-13
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:54.768380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.88393925514
 
< 0.1%
-0.74055584124
 
< 0.1%
0.41306388444
 
< 0.1%
-1.3900785164
 
< 0.1%
-1.5691900984
 
< 0.1%
-0.86657123214
 
< 0.1%
-0.53343007264
 
< 0.1%
-0.54665840824
 
< 0.1%
-0.35997524344
 
< 0.1%
-0.52344276934
 
< 0.1%
Other values (12001)13503
99.7%
ValueCountFrequency (%)
-2.5441131691
< 0.1%
-2.5380359521
< 0.1%
-2.5264504481
< 0.1%
-2.5040306351
< 0.1%
-2.4937420551
< 0.1%
-2.472396851
< 0.1%
-2.4507175971
< 0.1%
-2.4217963781
< 0.1%
-2.4171926371
< 0.1%
-2.4105640261
< 0.1%
ValueCountFrequency (%)
2.5435272851
< 0.1%
2.534163011
< 0.1%
2.5251496031
< 0.1%
2.5031949211
< 0.1%
2.4902226631
< 0.1%
2.4824708321
< 0.1%
2.4786793271
< 0.1%
2.4696915261
< 0.1%
2.469019031
< 0.1%
2.4550984721
< 0.1%

Perimeter
Real number (ℝ)

HIGH CORRELATION

Distinct13406
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.357803669 × 10-17
Minimum-2.641663779
Maximum2.691290481
Zeros0
Zeros (%)0.0%
Negative7064
Negative (%)52.2%
Memory size105.9 KiB
2022-11-02T17:39:54.845380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.641663779
5-th percentile-1.586620234
Q1-0.7655198618
median-0.06528688633
Q30.8449838314
95-th percentile1.444240996
Maximum2.691290481
Range5.33295426
Interquartile range (IQR)1.610503693

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)2.978247152 × 1016
Kurtosis-0.6310728687
Mean3.357803669 × 10-17
Median Absolute Deviation (MAD)0.8036662509
Skewness0.1026067629
Sum0
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:54.930880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.94226833123
 
< 0.1%
0.32947035332
 
< 0.1%
0.62895207092
 
< 0.1%
0.0094936771052
 
< 0.1%
-1.565734192
 
< 0.1%
1.0626049542
 
< 0.1%
-1.3952875862
 
< 0.1%
-0.90081988422
 
< 0.1%
-1.3297943152
 
< 0.1%
0.054037304032
 
< 0.1%
Other values (13396)13522
99.8%
ValueCountFrequency (%)
-2.6416637791
< 0.1%
-2.6392506191
< 0.1%
-2.6333317711
< 0.1%
-2.5965811121
< 0.1%
-2.5687848231
< 0.1%
-2.5670532681
< 0.1%
-2.5610447221
< 0.1%
-2.5320697481
< 0.1%
-2.5197506261
< 0.1%
-2.5173159181
< 0.1%
ValueCountFrequency (%)
2.6912904811
< 0.1%
2.6213580771
< 0.1%
2.6193182471
< 0.1%
2.5922148491
< 0.1%
2.57916111
< 0.1%
2.5621794331
< 0.1%
2.5385214521
< 0.1%
2.538134171
< 0.1%
2.5364434541
< 0.1%
2.5339768771
< 0.1%

MajorAxisLength
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct13543
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-2.539447477
Maximum2.681326871
Zeros0
Zeros (%)0.0%
Negative6991
Negative (%)51.6%
Memory size105.9 KiB
2022-11-02T17:39:55.015880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.539447477
5-th percentile-1.483029909
Q1-0.8514628026
median-0.05944217178
Q30.8783908654
95-th percentile1.412821871
Maximum2.681326871
Range5.220774348
Interquartile range (IQR)1.729853668

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)nan
Kurtosis-0.8606543352
Mean0
Median Absolute Deviation (MAD)0.8706858572
Skewness0.1410668239
Sum0
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:55.093379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.8857194881
 
< 0.1%
-0.010365782611
 
< 0.1%
0.025290375641
 
< 0.1%
0.046331785561
 
< 0.1%
-0.082680799371
 
< 0.1%
-0.065638396651
 
< 0.1%
0.056564002791
 
< 0.1%
-0.030207390661
 
< 0.1%
-0.021813747711
 
< 0.1%
-0.15743556781
 
< 0.1%
Other values (13533)13533
99.9%
ValueCountFrequency (%)
-2.5394474771
< 0.1%
-2.5293210561
< 0.1%
-2.4900444621
< 0.1%
-2.4707875191
< 0.1%
-2.4407937651
< 0.1%
-2.3928496091
< 0.1%
-2.3688573661
< 0.1%
-2.355718481
< 0.1%
-2.331476271
< 0.1%
-2.3307618471
< 0.1%
ValueCountFrequency (%)
2.6813268711
< 0.1%
2.6790327561
< 0.1%
2.640923051
< 0.1%
2.6282045661
< 0.1%
2.623997251
< 0.1%
2.6222808981
< 0.1%
2.6170963071
< 0.1%
2.6035834051
< 0.1%
2.599965571
< 0.1%
2.5979620451
< 0.1%

MinorAxisLength
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct13543
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.357803669 × 10-17
Minimum-3.773133569
Maximum2.776782145
Zeros0
Zeros (%)0.0%
Negative6622
Negative (%)48.9%
Memory size105.9 KiB
2022-11-02T17:39:55.172880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-3.773133569
5-th percentile-1.725034991
Q1-0.6198361442
median0.02037156823
Q30.6819800233
95-th percentile1.466099046
Maximum2.776782145
Range6.549915714
Interquartile range (IQR)1.301816167

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)-2.978247152 × 1016
Kurtosis0.04924339445
Mean-3.357803669 × 10-17
Median Absolute Deviation (MAD)0.6518031891
Skewness-0.0201715851
Sum0
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:55.279880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.70782535531
 
< 0.1%
0.17552213891
 
< 0.1%
0.1079189771
 
< 0.1%
0.083069809461
 
< 0.1%
0.25000158841
 
< 0.1%
0.2201016371
 
< 0.1%
0.097341195151
 
< 0.1%
0.19601045941
 
< 0.1%
0.18553840731
 
< 0.1%
0.34496956911
 
< 0.1%
Other values (13533)13533
99.9%
ValueCountFrequency (%)
-3.7731335691
< 0.1%
-3.2605577061
< 0.1%
-3.2484381021
< 0.1%
-3.1791133181
< 0.1%
-3.1306163411
< 0.1%
-3.0814257811
< 0.1%
-3.0783913421
< 0.1%
-3.0707483771
< 0.1%
-3.0700843031
< 0.1%
-3.047213571
< 0.1%
ValueCountFrequency (%)
2.7767821451
< 0.1%
2.7356038951
< 0.1%
2.7284321241
< 0.1%
2.7196959581
< 0.1%
2.7134107981
< 0.1%
2.6886377061
< 0.1%
2.6658220281
< 0.1%
2.6503195321
< 0.1%
2.6493612131
< 0.1%
2.6376510181
< 0.1%

AspectRation
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct13543
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-2.872390761
Maximum2.718775684
Zeros0
Zeros (%)0.0%
Negative6841
Negative (%)50.5%
Memory size105.9 KiB
2022-11-02T17:39:55.362380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.872390761
5-th percentile-1.764521394
Q1-0.5671204642
median-0.01153165488
Q30.5984932504
95-th percentile1.788141615
Maximum2.718775684
Range5.591166445
Interquartile range (IQR)1.165613715

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)nan
Kurtosis-0.2170020279
Mean0
Median Absolute Deviation (MAD)0.5823195206
Skewness-0.009759682583
Sum2.273736754 × 10-13
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:55.444380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.8266401871
 
< 0.1%
-0.15264366861
 
< 0.1%
-0.025110071941
 
< 0.1%
0.032503995971
 
< 0.1%
-0.34634383261
 
< 0.1%
-0.2857943081
 
< 0.1%
0.032366707791
 
< 0.1%
-0.20540945321
 
< 0.1%
-0.18098576941
 
< 0.1%
-0.57383087771
 
< 0.1%
Other values (13533)13533
99.9%
ValueCountFrequency (%)
-2.8723907611
< 0.1%
-2.7995772841
< 0.1%
-2.7647921071
< 0.1%
-2.732251111
< 0.1%
-2.7196174281
< 0.1%
-2.7180932671
< 0.1%
-2.6472732411
< 0.1%
-2.6471965681
< 0.1%
-2.622972741
< 0.1%
-2.6195632581
< 0.1%
ValueCountFrequency (%)
2.7187756841
< 0.1%
2.6152103491
< 0.1%
2.6114886111
< 0.1%
2.6010719831
< 0.1%
2.5523971661
< 0.1%
2.5378049541
< 0.1%
2.5179504071
< 0.1%
2.5107911261
< 0.1%
2.5041827821
< 0.1%
2.499623741
< 0.1%

Eccentricity
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct13543
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.511011651 × 10-16
Minimum-3.351992598
Maximum2.495349533
Zeros0
Zeros (%)0.0%
Negative6982
Negative (%)51.6%
Memory size105.9 KiB
2022-11-02T17:39:55.530880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-3.351992598
5-th percentile-1.735082213
Q1-0.5754540149
median-0.03530007985
Q30.6137490623
95-th percentile1.791113771
Maximum2.495349533
Range5.847342131
Interquartile range (IQR)1.189203077

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)-6.618327005 × 1015
Kurtosis-0.2381997868
Mean-1.511011651 × 10-16
Median Absolute Deviation (MAD)0.5905012481
Skewness-0.005182552134
Sum-1.818989404 × 10-12
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:55.609880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.8020243531
 
< 0.1%
-0.17776666781
 
< 0.1%
-0.04918093821
 
< 0.1%
0.0099556181371
 
< 0.1%
-0.36663079251
 
< 0.1%
-0.30833236131
 
< 0.1%
0.0098139637381
 
< 0.1%
-0.22994904661
 
< 0.1%
-0.20587345721
 
< 0.1%
-0.58173731161
 
< 0.1%
Other values (13533)13533
99.9%
ValueCountFrequency (%)
-3.3519925981
< 0.1%
-3.1805252021
< 0.1%
-3.1072459791
< 0.1%
-3.0424292411
< 0.1%
-3.0181067541
< 0.1%
-3.0152018481
< 0.1%
-2.8863849251
< 0.1%
-2.8862513561
< 0.1%
-2.8446136141
< 0.1%
-2.8388399451
< 0.1%
ValueCountFrequency (%)
2.4953495331
< 0.1%
2.4268006781
< 0.1%
2.4242935351
< 0.1%
2.4172601131
< 0.1%
2.3840751121
< 0.1%
2.374023621
< 0.1%
2.3602706011
< 0.1%
2.3552897041
< 0.1%
2.3506818581
< 0.1%
2.3474971991
< 0.1%

ConvexArea
Real number (ℝ)

HIGH CORRELATION

Distinct12066
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.715607338 × 10-17
Minimum-2.534132994
Maximum2.552584673
Zeros0
Zeros (%)0.0%
Negative7054
Negative (%)52.1%
Memory size105.9 KiB
2022-11-02T17:39:55.696380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.534132994
5-th percentile-1.618048448
Q1-0.7287412959
median-0.05445769881
Q30.8041226236
95-th percentile1.475015849
Maximum2.552584673
Range5.086717667
Interquartile range (IQR)1.532863919

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)-1.489123576 × 1016
Kurtosis-0.54962645
Mean-6.715607338 × 10-17
Median Absolute Deviation (MAD)0.7532120679
Skewness0.1182870171
Sum-9.094947018 × 10-13
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:55.778880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.69712970575
 
< 0.1%
-0.19200001174
 
< 0.1%
-0.11479723444
 
< 0.1%
-0.52964365234
 
< 0.1%
0.12730605474
 
< 0.1%
-1.2425234034
 
< 0.1%
0.099507554134
 
< 0.1%
-0.30505965834
 
< 0.1%
-1.3933868213
 
< 0.1%
-0.6698474823
 
< 0.1%
Other values (12056)13504
99.7%
ValueCountFrequency (%)
-2.5341329941
< 0.1%
-2.5221880281
< 0.1%
-2.5150049921
< 0.1%
-2.4929659931
< 0.1%
-2.4836603331
< 0.1%
-2.4656287671
< 0.1%
-2.4293243791
< 0.1%
-2.4126519951
< 0.1%
-2.4122524251
< 0.1%
-2.3935026131
< 0.1%
ValueCountFrequency (%)
2.5525846731
< 0.1%
2.5360903641
< 0.1%
2.517507611
< 0.1%
2.4968915651
< 0.1%
2.4854062461
< 0.1%
2.4776846821
< 0.1%
2.4746972971
< 0.1%
2.4611766681
< 0.1%
2.4600393751
< 0.1%
2.4553193091
< 0.1%

EquivDiameter
Real number (ℝ)

HIGH CORRELATION

Distinct12011
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.715607338 × 10-17
Minimum-2.849788999
Maximum2.702064001
Zeros0
Zeros (%)0.0%
Negative6882
Negative (%)50.8%
Memory size105.9 KiB
2022-11-02T17:39:55.859380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.849788999
5-th percentile-1.679309142
Q1-0.686852075
median-0.02024268553
Q30.766979219
95-th percentile1.427818472
Maximum2.702064001
Range5.551852999
Interquartile range (IQR)1.453831294

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)-1.489123576 × 1016
Kurtosis-0.2933899429
Mean-6.715607338 × 10-17
Median Absolute Deviation (MAD)0.7224230361
Skewness0.04233645978
Sum-1.364242053 × 10-12
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:55.934380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.8536186814
 
< 0.1%
-0.70344954134
 
< 0.1%
0.41159208164
 
< 0.1%
-1.408239694
 
< 0.1%
-1.6145339084
 
< 0.1%
-0.83527437734
 
< 0.1%
-0.49146923594
 
< 0.1%
-0.50483871684
 
< 0.1%
-0.31821648524
 
< 0.1%
-0.48139030624
 
< 0.1%
Other values (12001)13503
99.7%
ValueCountFrequency (%)
-2.8497889991
< 0.1%
-2.8414065641
< 0.1%
-2.8254534191
< 0.1%
-2.794681471
< 0.1%
-2.7806038791
< 0.1%
-2.7514850521
< 0.1%
-2.7220301161
< 0.1%
-2.6829214311
< 0.1%
-2.6767154561
< 0.1%
-2.6677892171
< 0.1%
ValueCountFrequency (%)
2.7020640011
< 0.1%
2.6888430351
< 0.1%
2.6761664661
< 0.1%
2.6454870891
< 0.1%
2.627489321
< 0.1%
2.6167795691
< 0.1%
2.6115535021
< 0.1%
2.599196781
< 0.1%
2.5982739951
< 0.1%
2.5792278031
< 0.1%

Extent
Real number (ℝ)

HIGH CORRELATION

Distinct13535
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.777529128 × 10-17
Minimum-2.66017277
Maximum3.684092083
Zeros0
Zeros (%)0.0%
Negative6651
Negative (%)49.1%
Memory size105.9 KiB
2022-11-02T17:39:56.017881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.66017277
5-th percentile-1.648236025
Q1-0.7831073779
median0.02966873109
Q30.7589095359
95-th percentile1.57583431
Maximum3.684092083
Range6.344264853
Interquartile range (IQR)1.542016914

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)2.647330802 × 1016
Kurtosis-0.5732839173
Mean3.777529128 × 10-17
Median Absolute Deviation (MAD)0.7731394507
Skewness-0.05777768474
Sum4.547473509 × 10-13
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:56.101881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.46219013572
 
< 0.1%
-0.68172661662
 
< 0.1%
0.57031232422
 
< 0.1%
-0.83261233172
 
< 0.1%
-0.3705701572
 
< 0.1%
-1.1831958722
 
< 0.1%
0.49317836812
 
< 0.1%
-0.87305561642
 
< 0.1%
0.97397795391
 
< 0.1%
0.54541283231
 
< 0.1%
Other values (13525)13525
99.9%
ValueCountFrequency (%)
-2.660172771
< 0.1%
-2.5596677971
< 0.1%
-2.5587858971
< 0.1%
-2.5275085211
< 0.1%
-2.5271704941
< 0.1%
-2.5096209251
< 0.1%
-2.5080716131
< 0.1%
-2.4990672021
< 0.1%
-2.4929219711
< 0.1%
-2.4894721231
< 0.1%
ValueCountFrequency (%)
3.6840920831
< 0.1%
3.3523214811
< 0.1%
3.1199072911
< 0.1%
3.0963115491
< 0.1%
3.0831294961
< 0.1%
3.0337661351
< 0.1%
3.0136185351
< 0.1%
2.9473160941
< 0.1%
2.9210651681
< 0.1%
2.8559902191
< 0.1%

Solidity
Real number (ℝ)

HIGH CORRELATION

Distinct13526
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.308490022 × 10-17
Minimum-3.79266383
Maximum3.322939434
Zeros0
Zeros (%)0.0%
Negative6719
Negative (%)49.6%
Memory size105.9 KiB
2022-11-02T17:39:56.188380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-3.79266383
5-th percentile-1.659095973
Q1-0.6793153916
median0.008527680111
Q30.6743746288
95-th percentile1.66397696
Maximum3.322939434
Range7.115603265
Interquartile range (IQR)1.35369002

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)4.331995858 × 1016
Kurtosis-0.2243718133
Mean2.308490022 × 10-17
Median Absolute Deviation (MAD)0.6754735082
Skewness-0.02826281864
Sum3.410605132 × 10-13
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:56.284382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.51262100342
 
< 0.1%
-0.069575766142
 
< 0.1%
0.43121894842
 
< 0.1%
0.26837540152
 
< 0.1%
0.94490441362
 
< 0.1%
0.56126157962
 
< 0.1%
-0.37596307132
 
< 0.1%
0.79679997912
 
< 0.1%
0.75638745562
 
< 0.1%
1.0186609292
 
< 0.1%
Other values (13516)13523
99.9%
ValueCountFrequency (%)
-3.792663831
< 0.1%
-3.2766773971
< 0.1%
-3.2499816441
< 0.1%
-3.1934988641
< 0.1%
-3.1911082011
< 0.1%
-3.1642770821
< 0.1%
-3.1248565681
< 0.1%
-3.1225129111
< 0.1%
-3.0354558511
< 0.1%
-2.9920017981
< 0.1%
ValueCountFrequency (%)
3.3229394341
< 0.1%
3.1152545461
< 0.1%
3.034421181
< 0.1%
3.0031734191
< 0.1%
2.9149341011
< 0.1%
2.914662621
< 0.1%
2.8929104611
< 0.1%
2.8240965181
< 0.1%
2.8068751141
< 0.1%
2.7977737541
< 0.1%

roundness
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct13543
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-4.16508363
Maximum2.49957336
Zeros0
Zeros (%)0.0%
Negative6604
Negative (%)48.8%
Memory size105.9 KiB
2022-11-02T17:39:56.384380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-4.16508363
5-th percentile-1.561397515
Q1-0.7675563641
median0.03365732451
Q30.7071627329
95-th percentile1.692138069
Maximum2.49957336
Range6.66465699
Interquartile range (IQR)1.474719097

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)nan
Kurtosis-0.5393184188
Mean0
Median Absolute Deviation (MAD)0.7316918187
Skewness-0.03854900317
Sum0
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:56.473382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6580887211
 
< 0.1%
0.50500797711
 
< 0.1%
0.28439823821
 
< 0.1%
0.21314904231
 
< 0.1%
0.62031743551
 
< 0.1%
0.6377201131
 
< 0.1%
0.27262290991
 
< 0.1%
0.31439822421
 
< 0.1%
-0.044882625751
 
< 0.1%
0.32788522341
 
< 0.1%
Other values (13533)13533
99.9%
ValueCountFrequency (%)
-4.165083631
< 0.1%
-3.6261801971
< 0.1%
-3.5003876531
< 0.1%
-3.4646411751
< 0.1%
-3.4492437371
< 0.1%
-3.3132872381
< 0.1%
-3.3016876651
< 0.1%
-3.2414074061
< 0.1%
-3.2114662631
< 0.1%
-3.2011947691
< 0.1%
ValueCountFrequency (%)
2.499573361
< 0.1%
2.4256953141
< 0.1%
2.4036020091
< 0.1%
2.3962435651
< 0.1%
2.392856221
< 0.1%
2.3777374721
< 0.1%
2.3774193141
< 0.1%
2.3733506741
< 0.1%
2.3473077481
< 0.1%
2.344959251
< 0.1%

Compactness
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct13543
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.357803669 × 10-17
Minimum-2.625977594
Maximum3.00703326
Zeros0
Zeros (%)0.0%
Negative6600
Negative (%)48.7%
Memory size105.9 KiB
2022-11-02T17:39:56.569880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.625977594
5-th percentile-1.787749376
Q1-0.6009897421
median0.02438124913
Q30.5585793864
95-th percentile1.760090663
Maximum3.00703326
Range5.633010854
Interquartile range (IQR)1.159569128

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)2.978247152 × 1016
Kurtosis-0.2087203907
Mean3.357803669 × 10-17
Median Absolute Deviation (MAD)0.5772308036
Skewness0.009205494274
Sum0
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:56.658883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8267772291
 
< 0.1%
0.16360192641
 
< 0.1%
0.047374694821
 
< 0.1%
-0.012494097911
 
< 0.1%
0.35444703661
 
< 0.1%
0.30655594051
 
< 0.1%
-0.039832526121
 
< 0.1%
0.20694356351
 
< 0.1%
0.18334605851
 
< 0.1%
0.56554864141
 
< 0.1%
Other values (13533)13533
99.9%
ValueCountFrequency (%)
-2.6259775941
< 0.1%
-2.5464587511
< 0.1%
-2.5393444991
< 0.1%
-2.5266391461
< 0.1%
-2.4901371231
< 0.1%
-2.4855457271
< 0.1%
-2.483044291
< 0.1%
-2.4425453611
< 0.1%
-2.4385614791
< 0.1%
-2.4339662621
< 0.1%
ValueCountFrequency (%)
3.007033261
< 0.1%
2.916404531
< 0.1%
2.8816980071
< 0.1%
2.838798381
< 0.1%
2.8253513251
< 0.1%
2.8205678221
< 0.1%
2.739636981
< 0.1%
2.7389343151
< 0.1%
2.7112279431
< 0.1%
2.706620351
< 0.1%

ShapeFactor1
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct13543
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.715607338 × 10-17
Minimum-2.805680907
Maximum4.356165556
Zeros0
Zeros (%)0.0%
Negative6875
Negative (%)50.8%
Memory size105.9 KiB
2022-11-02T17:39:56.747880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.805680907
5-th percentile-1.48781022
Q1-0.6519365874
median-0.01302726104
Q30.5946138007
95-th percentile1.715928757
Maximum4.356165556
Range7.161846463
Interquartile range (IQR)1.246550388

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)1.489123576 × 1016
Kurtosis0.276768804
Mean6.715607338 × 10-17
Median Absolute Deviation (MAD)0.6234087533
Skewness0.01117682259
Sum4.547473509 × 10-13
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:57.194382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6570973771
 
< 0.1%
-0.16220817431
 
< 0.1%
-0.10420484241
 
< 0.1%
-0.077130635231
 
< 0.1%
-0.23774328861
 
< 0.1%
-0.2177296411
 
< 0.1%
-0.065470986521
 
< 0.1%
-0.17542998131
 
< 0.1%
-0.16527977241
 
< 0.1%
-0.32399445971
 
< 0.1%
Other values (13533)13533
99.9%
ValueCountFrequency (%)
-2.8056809071
< 0.1%
-2.7573971721
< 0.1%
-2.7546549911
< 0.1%
-2.7432916561
< 0.1%
-2.7286310951
< 0.1%
-2.7241112361
< 0.1%
-2.7032670141
< 0.1%
-2.6927149681
< 0.1%
-2.6832536141
< 0.1%
-2.6766602211
< 0.1%
ValueCountFrequency (%)
4.3561655561
< 0.1%
3.6397577791
< 0.1%
3.5469651231
< 0.1%
3.4506694911
< 0.1%
3.4155789621
< 0.1%
3.3515125871
< 0.1%
3.3481454951
< 0.1%
3.3420114781
< 0.1%
3.3311778661
< 0.1%
3.2802106061
< 0.1%

ShapeFactor2
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct13543
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.036705503 × 10-17
Minimum-2.189008127
Maximum2.772052763
Zeros0
Zeros (%)0.0%
Negative6556
Negative (%)48.4%
Memory size105.9 KiB
2022-11-02T17:39:57.290880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.189008127
5-th percentile-1.450148964
Q1-0.9512690094
median0.05279886907
Q30.8001313724
95-th percentile1.580845719
Maximum2.772052763
Range4.961060889
Interquartile range (IQR)1.751400382

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)-1.985498101 × 1016
Kurtosis-1.050078034
Mean-5.036705503 × 10-17
Median Absolute Deviation (MAD)0.8707679565
Skewness0.06294451057
Sum-9.094947018 × 10-13
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:57.375380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1304267271
 
< 0.1%
0.087223876421
 
< 0.1%
0.013375102191
 
< 0.1%
-0.026300227841
 
< 0.1%
0.21724840671
 
< 0.1%
0.18529000331
 
< 0.1%
-0.04479457461
 
< 0.1%
0.11888183511
 
< 0.1%
0.10315696241
 
< 0.1%
0.35822386421
 
< 0.1%
Other values (13533)13533
99.9%
ValueCountFrequency (%)
-2.1890081271
< 0.1%
-2.1828690641
< 0.1%
-2.1810091451
< 0.1%
-2.1709807551
< 0.1%
-2.1329495211
< 0.1%
-2.1315607191
< 0.1%
-2.1279548671
< 0.1%
-2.1074931021
< 0.1%
-2.0990756291
< 0.1%
-2.0965080241
< 0.1%
ValueCountFrequency (%)
2.7720527631
< 0.1%
2.6607649161
< 0.1%
2.6490420811
< 0.1%
2.6265312211
< 0.1%
2.5790830381
< 0.1%
2.5346298271
< 0.1%
2.4910362141
< 0.1%
2.4711477811
< 0.1%
2.4632049491
< 0.1%
2.4387629281
< 0.1%

ShapeFactor3
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct13543
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.715607338 × 10-17
Minimum-2.584342953
Maximum3.024851083
Zeros0
Zeros (%)0.0%
Negative6584
Negative (%)48.6%
Memory size105.9 KiB
2022-11-02T17:39:57.458880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.584342953
5-th percentile-1.787388711
Q1-0.6046946858
median0.02801683691
Q30.5608613921
95-th percentile1.756211403
Maximum3.024851083
Range5.609194037
Interquartile range (IQR)1.165556078

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)1.489123576 × 1016
Kurtosis-0.2230537081
Mean6.715607338 × 10-17
Median Absolute Deviation (MAD)0.5793010371
Skewness0.007776509232
Sum1.364242053 × 10-12
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:57.534879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8231213861
 
< 0.1%
0.16762989781
 
< 0.1%
0.051129449991
 
< 0.1%
-0.0090943192411
 
< 0.1%
0.35800864791
 
< 0.1%
0.31031869571
 
< 0.1%
-0.036639523681
 
< 0.1%
0.21095175551
 
< 0.1%
0.18737229911
 
< 0.1%
0.56777823391
 
< 0.1%
Other values (13533)13533
99.9%
ValueCountFrequency (%)
-2.5843429531
< 0.1%
-2.5104800791
< 0.1%
-2.5038529851
< 0.1%
-2.4920099951
< 0.1%
-2.4579311391
< 0.1%
-2.453638851
< 0.1%
-2.4512998361
< 0.1%
-2.4133784221
< 0.1%
-2.40964281
< 0.1%
-2.4053327661
< 0.1%
ValueCountFrequency (%)
3.0248510831
< 0.1%
2.9312523751
< 0.1%
2.8954714451
< 0.1%
2.8512914761
< 0.1%
2.8374538871
< 0.1%
2.8325326971
< 0.1%
2.7493701731
< 0.1%
2.7486489371
< 0.1%
2.7202212121
< 0.1%
2.7154957251
< 0.1%

ShapeFactor4
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct13543
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.036705503 × 10-17
Minimum-2.851452456
Maximum2.06580388
Zeros0
Zeros (%)0.0%
Negative6506
Negative (%)48.0%
Memory size105.9 KiB
2022-11-02T17:39:57.616880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-2.851452456
5-th percentile-1.654055341
Q1-0.7812318385
median0.05673802321
Q30.8150537248
95-th percentile1.519834901
Maximum2.06580388
Range4.917256336
Interquartile range (IQR)1.596285563

Descriptive statistics

Standard deviation1.000036921
Coefficient of variation (CV)-1.985498101 × 1016
Kurtosis-0.8491223221
Mean-5.036705503 × 10-17
Median Absolute Deviation (MAD)0.794833231
Skewness-0.1776515729
Sum0
Variance1.000073844
MonotonicityNot monotonic
2022-11-02T17:39:57.701880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3358419771
 
< 0.1%
-0.011100492391
 
< 0.1%
0.42654587661
 
< 0.1%
0.12080097411
 
< 0.1%
0.41116429281
 
< 0.1%
1.2684782981
 
< 0.1%
-1.0442529141
 
< 0.1%
-0.40230875221
 
< 0.1%
-0.41575995181
 
< 0.1%
0.0215962661
 
< 0.1%
Other values (13533)13533
99.9%
ValueCountFrequency (%)
-2.8514524561
< 0.1%
-2.8200008651
< 0.1%
-2.802160321
< 0.1%
-2.7441306041
< 0.1%
-2.7137615661
< 0.1%
-2.7075062531
< 0.1%
-2.7059145281
< 0.1%
-2.7058723571
< 0.1%
-2.6985811371
< 0.1%
-2.6891230311
< 0.1%
ValueCountFrequency (%)
2.065803881
< 0.1%
2.0475930981
< 0.1%
2.0462235661
< 0.1%
2.025459681
< 0.1%
2.0206480781
< 0.1%
2.0176836121
< 0.1%
2.0092581371
< 0.1%
2.0036742291
< 0.1%
1.9919366691
< 0.1%
1.9830098761
< 0.1%

Interactions

2022-11-02T17:39:53.213880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:34.546880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:35.738881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:37.720880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:38.819380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:39.983380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:41.234380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:42.382880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:43.630381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:44.803879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:45.934380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-02T17:39:38.611880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:39.766380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:41.020379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:42.163380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:43.429881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:44.593880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:45.725880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:46.881380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:48.230880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:49.346381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:50.484880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:51.628379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:53.010380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:54.191880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:35.583880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:36.731380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:38.685380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:39.836880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:41.094880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:42.230380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:43.499880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:44.664380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:45.796880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:46.954380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:48.300380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:49.413380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:50.566380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:51.696880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:53.076380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:54.258379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:35.650881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:37.649879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:38.751880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:39.910879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:41.166379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:42.306380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:43.564380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:44.732379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:45.866384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:47.025380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:48.368880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:49.479880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:50.646380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:51.762879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-02T17:39:53.143380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-11-02T17:39:57.797379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-02T17:39:57.938880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-02T17:39:58.067380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-02T17:39:58.193880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-02T17:39:58.319880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-02T17:39:54.388380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-02T17:39:54.564880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AreaPerimeterMajorAxisLengthMinorAxisLengthAspectRationEccentricityConvexAreaEquivDiameterExtentSolidityroundnessCompactnessShapeFactor1ShapeFactor2ShapeFactor3ShapeFactor4
0-1.537858-1.661508-1.885719-0.707825-1.826640-1.802024-1.537180-1.5780400.1284660.2081261.6580891.8267770.6570972.1304271.8231211.335842
1-1.499254-1.375625-2.082763-0.336054-2.421818-2.532863-1.486013-1.5333160.673735-0.8136590.0996712.4740550.3040642.6490422.4777311.143895
2-1.426611-1.517065-1.768962-0.617890-1.754192-1.724066-1.428727-1.4498650.5057590.4806271.4103781.7533890.5677702.0028621.7494921.571212
3-1.357148-1.297382-1.825661-0.344657-2.082529-2.092748-1.316622-1.3709120.636220-1.8130240.4317852.0662780.3412542.2157972.064195-0.670601
4-1.342692-1.558225-2.048274-0.055485-2.647197-2.886251-1.349595-1.3545830.3674881.0763512.3449592.7396370.0363952.7720532.7493701.642302
5-1.327523-1.406717-1.775573-0.384754-1.979414-1.972362-1.329908-1.3374870.4382500.4607171.3150931.9926750.3445242.1374181.9899761.692544
6-1.306012-1.065119-1.813314-0.285130-2.123430-2.141920-1.290390-1.3133070.090645-0.969892-0.4779472.1469130.2519392.2504522.1456481.558942
7-1.301463-1.459466-1.764718-0.335944-2.011712-2.009553-1.303485-1.3082040.3027690.4033951.8851472.0207330.3045442.1444252.0182541.089530
8-1.283535-1.399115-1.751370-0.319443-2.010167-2.007763-1.282531-1.2881240.3261860.0587771.5650642.0235810.2848612.1358532.0211261.492004
9-1.267510-1.437011-1.660432-0.409460-1.805512-1.779119-1.274477-1.2702190.6653891.0360781.9656671.8070490.3690921.9515761.8033181.567687

Last rows

AreaPerimeterMajorAxisLengthMinorAxisLengthAspectRationEccentricityConvexAreaEquivDiameterExtentSolidityroundnessCompactnessShapeFactor1ShapeFactor2ShapeFactor3ShapeFactor4
13533-0.238961-0.221885-0.195321-0.197505-0.028635-0.052779-0.246007-0.1995361.7181260.5733320.1098490.0504800.1822620.1507400.0542490.403766
13534-0.238578-0.240621-0.114481-0.3201480.1988560.184115-0.248280-0.1991630.3889760.9365680.226246-0.1671790.2934950.004103-0.1652350.749021
13535-0.238425-0.231530-0.160606-0.2462970.0666900.045337-0.243964-0.199014-0.0625720.3842210.171064-0.0421500.2285350.088098-0.0389760.376521
13536-0.236816-0.280658-0.179582-0.2058590.001492-0.021953-0.239958-0.1974480.2538380.1088030.4935420.0123440.1992320.1240640.015909-0.201546
13537-0.236816-0.301396-0.3940230.068663-0.586985-0.594047-0.247371-0.197448-0.5756711.0610140.6334950.590731-0.0733830.5166320.5927680.985003
13538-0.234749-0.308912-0.184609-0.2125550.000950-0.022510-0.243586-0.195436-0.8504110.8129630.6979310.0300980.1881990.1350860.0337651.115223
13539-0.234443-0.325418-0.307031-0.040377-0.345665-0.365981-0.244645-0.1951381.1663361.0082700.8158290.3609370.0281700.3587270.3644671.011126
13540-0.231537-0.311722-0.307671-0.023969-0.363957-0.383487-0.238977-0.192312-0.5914740.6226630.7373270.3686700.0218020.3626210.3721630.225023
13541-0.230926-0.278511-0.275540-0.055609-0.285071-0.307633-0.231592-0.191717-0.992589-0.1439610.5136850.2832550.0611400.3043700.287098-0.383955
13542-0.230009-0.216666-0.079536-0.3570170.2797360.270328-0.236638-0.1908260.8224470.5172130.125101-0.2448150.325573-0.051768-0.2438030.987158